Bump version: 0.23.1-beta.2 → 0.23.1

2026-01-13 07:12:57 +00:00 · 2026-01-02 17:38:59 +00:00
33 changed files with 661 additions and 1410 deletions
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -15,39 +15,39 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"

 [workspace.dependencies]
-lance = { "version" = "=2.0.0-beta.6", default-features = false, "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=2.0.0-beta.6", default-features = false, "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=2.0.0-beta.6", default-features = false, "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=2.0.0-beta.6", "tag" = "v2.0.0-beta.6", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=1.0.1", default-features = false }
+lance-core = "=1.0.1"
+lance-datagen = "=1.0.1"
+lance-file = "=1.0.1"
+lance-io = { "version" = "=1.0.1", default-features = false }
+lance-index = "=1.0.1"
+lance-linalg = "=1.0.1"
+lance-namespace = "=1.0.1"
+lance-namespace-impls = { "version" = "=1.0.1", default-features = false }
+lance-table = "=1.0.1"
+lance-testing = "=1.0.1"
+lance-datafusion = "=1.0.1"
+lance-encoding = "=1.0.1"
+lance-arrow = "=1.0.1"
 ahash = "0.8"
 # Note that this one does not include pyarrow
-arrow = { version = "57.1", optional = false }
-arrow-array = "57.1"
-arrow-data = "57.1"
-arrow-ipc = "57.1"
-arrow-ord = "57.1"
-arrow-schema = "57.1"
-arrow-select = "57.1"
-arrow-cast = "57.1"
+arrow = { version = "56.2", optional = false }
+arrow-array = "56.2"
+arrow-data = "56.2"
+arrow-ipc = "56.2"
+arrow-ord = "56.2"
+arrow-schema = "56.2"
+arrow-select = "56.2"
+arrow-cast = "56.2"
 async-trait = "0"
-datafusion = { version = "51.0", default-features = false }
-datafusion-catalog = "51.0"
-datafusion-common = { version = "51.0", default-features = false }
-datafusion-execution = "51.0"
-datafusion-expr = "51.0"
-datafusion-physical-plan = "51.0"
+datafusion = { version = "50.1", default-features = false }
+datafusion-catalog = "50.1"
+datafusion-common = { version = "50.1", default-features = false }
+datafusion-execution = "50.1"
+datafusion-expr = "50.1"
+datafusion-physical-plan = "50.1"
 env_logger = "0.11"
-half = { "version" = "2.7.1", default-features = false, features = [
+half = { "version" = "2.6.0", default-features = false, features = [
    "num-traits",
 ] }
 futures = "0"
--- a/ci/run_with_test_connection.sh
+++ b/ci/run_with_test_connection.sh
@@ -16,7 +16,7 @@ check_command_exists() {
 }

 if [[ ! -e ./lancedb ]]; then
-    if [[ x${SOPHON_READ_TOKEN} != "x" ]]; then
+    if [[ -v SOPHON_READ_TOKEN ]]; then
        INPUT="lancedb-linux-x64"
        gh release \
            --repo lancedb/lancedb \
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -11,7 +11,7 @@ watch:
 theme:
  name: "material"
  logo: assets/logo.png
-  favicon: assets/favicon.ico
+  favicon: assets/logo.png
  palette:
    # Palette toggle for light mode
    - scheme: lancedb
@@ -32,6 +32,8 @@ theme:
    - content.tooltips
    - toc.follow
    - navigation.top
+    - navigation.tabs
+    - navigation.tabs.sticky
    - navigation.footer
    - navigation.tracking
    - navigation.instant
@@ -113,13 +115,12 @@ markdown_extensions:
      emoji_index: !!python/name:material.extensions.emoji.twemoji
      emoji_generator: !!python/name:material.extensions.emoji.to_svg
  - markdown.extensions.toc:
-      toc_depth: 3
-      permalink: true
-      permalink_title: Anchor link to this section
+      baselevel: 1
+      permalink: ""

 nav:
-  - Documentation:
-      - SDK Reference: index.md
+  - API reference:
+      - Overview: index.md
      - Python: python/python.md
      - Javascript/TypeScript: js/globals.md
      - Java: java/java.md
--- a/docs/src/assets/favicon.ico
+++ b/docs/src/assets/favicon.ico
--- a/docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/voyageai_multimodal_embedding.md
+++ b/docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/voyageai_multimodal_embedding.md
@@ -1,111 +0,0 @@
-# VoyageAI Embeddings : Multimodal
-
-VoyageAI embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list
-under [https://docs.voyageai.com/docs/multimodal-embeddings](https://docs.voyageai.com/docs/multimodal-embeddings)
-
-Supported multimodal models:
-
- `voyage-multimodal-3` - 1024 dimensions (text + images)
- `voyage-multimodal-3.5` - Flexible dimensions (256, 512, 1024 default, 2048). Supports text, images, and video.
-
-### Video Support (voyage-multimodal-3.5)
-
-The `voyage-multimodal-3.5` model supports video input through:
- Video URLs (`.mp4`, `.webm`, `.mov`, `.avi`, `.mkv`, `.m4v`, `.gif`)
- Video file paths
-
-Constraints: Max 20MB video size.
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value           | Description                               |
-|---|---|-------------------------|-------------------------------------------|
-| `name` | `str` | `"voyage-multimodal-3"` | The model ID of the VoyageAI model to use |
-| `output_dimension` | `int` | `None` | Output dimension for voyage-multimodal-3.5. Valid: 256, 512, 1024, 2048 |
-
-Usage Example:
-
-```python
-import base64
-import os
-from io import BytesIO
-
-import requests
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-import pandas as pd
-
-os.environ['VOYAGE_API_KEY'] = 'YOUR_VOYAGE_API_KEY'
-
-db = lancedb.connect(".lancedb")
-func = get_registry().get("voyageai").create(name="voyage-multimodal-3")
-
-
-def image_to_base64(image_bytes: bytes):
-    buffered = BytesIO(image_bytes)
-    img_str = base64.b64encode(buffered.getvalue())
-    return img_str.decode("utf-8")
-
-
-class Images(LanceModel):
-    label: str
-    image_uri: str = func.SourceField()  # image uri as the source
-    image_bytes: str = func.SourceField()  # image bytes base64 encoded as the source
-    vector: Vector(func.ndims()) = func.VectorField()  # vector column
-    vec_from_bytes: Vector(func.ndims()) = func.VectorField()  # Another vector column
-
-
-if "images" in db.table_names():
-    db.drop_table("images")
-table = db.create_table("images", schema=Images)
-labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-uris = [
-    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-]
-# get each uri as bytes
-images_bytes = [image_to_base64(requests.get(uri).content) for uri in uris]
-table.add(
-    pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": images_bytes})
-)
-```
-Now we can search using text from both the default vector column and the custom vector column
-```python
-
-# text search
-actual = table.search("man's best friend", "vec_from_bytes").limit(1).to_pydantic(Images)[0]
-print(actual.label) # prints "dog"
-
-frombytes = (
-    table.search("man's best friend", vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(frombytes.label)
-
-```
-
-Because we're using a multi-modal embedding function, we can also search using images
-
-```python
-# image search
-query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
-image_bytes = requests.get(query_image_uri).content
-query_image = Image.open(BytesIO(image_bytes))
-actual = table.search(query_image, "vec_from_bytes").limit(1).to_pydantic(Images)[0]
-print(actual.label == "dog")
-
-# image search using a custom vector column
-other = (
-    table.search(query_image, vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(actual.label)
-
-```
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -1,12 +1,8 @@
-# SDK Reference
+# API Reference

-This site contains the API reference for the client SDKs supported by [LanceDB](https://lancedb.com).
+This page contains the API reference for the SDKs supported by the LanceDB team.

 - [Python](python/python.md)
 - [JavaScript/TypeScript](js/globals.md)
 - [Java](java/java.md)
- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)
-
-!!! info "LanceDB Documentation"
-
-    If you're looking for the full documentation of LanceDB, visit [docs.lancedb.com](https://docs.lancedb.com).
+- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)
--- a/docs/src/styles/extra.css
+++ b/docs/src/styles/extra.css
@@ -85,26 +85,17 @@

 /* Header gradient (only header area) */
 .md-header {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
  box-shadow: inset 0 1px 0 rgba(255,255,255,0.08), 0 1px 0 rgba(0,0,0,0.08);
 }

-/* Improve brand title contrast on the lavender side */
-.md-header__title,
-.md-header__topic,
-.md-header__title .md-ellipsis,
-.md-header__topic .md-ellipsis {
-  color: #2b1b3a;
-  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);
-}
-
 /* Same colors as header for tabs (that hold the text) */
 .md-tabs {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
 }

 /* Dark scheme variant */
 [data-md-color-scheme="slate"] .md-header,
 [data-md-color-scheme="slate"] .md-tabs {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
 }
--- a/nodejs/package-lock.json
+++ b/nodejs/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.23.1",
+  "version": "0.23.1-beta.1",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.23.1",
+      "version": "0.23.1-beta.1",
      "cpu": [
        "x64",
        "arm64"
--- a/python/Cargo.toml
+++ b/python/Cargo.toml
@@ -14,15 +14,15 @@ name = "_lancedb"
 crate-type = ["cdylib"]

 [dependencies]
-arrow = { workspace = true, features = ["pyarrow"] }
+arrow = { version = "56.2", features = ["pyarrow"] }
 async-trait = "0.1"
 lancedb = { path = "../rust/lancedb", default-features = false }
 lance-core.workspace = true
 lance-namespace.workspace = true
 lance-io.workspace = true
 env_logger.workspace = true
-pyo3 = { version = "0.26", features = ["extension-module", "abi3-py39"] }
-pyo3-async-runtimes = { version = "0.26", features = [
+pyo3 = { version = "0.25", features = ["extension-module", "abi3-py39"] }
+pyo3-async-runtimes = { version = "0.25", features = [
    "attributes",
    "tokio-runtime",
 ] }
@@ -32,7 +32,7 @@ snafu.workspace = true
 tokio = { version = "1.40", features = ["sync"] }

 [build-dependencies]
-pyo3-build-config = { version = "0.26", features = [
+pyo3-build-config = { version = "0.25", features = [
    "extension-module",
    "abi3-py39",
 ] }
--- a/python/python/lancedb/init.py
+++ b/python/python/lancedb/init.py
@@ -13,7 +13,6 @@ __version__ = importlib.metadata.version("lancedb")

 from ._lancedb import connect as lancedb_connect
 from .common import URI, sanitize_uri
-from urllib.parse import urlparse
 from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .io import StorageOptionsProvider
 from .remote import ClientConfig
@@ -29,39 +28,6 @@ from .namespace import (
 )


-def _check_s3_bucket_with_dots(
-    uri: str, storage_options: Optional[Dict[str, str]]
-) -> None:
-    """
-    Check if an S3 URI has a bucket name containing dots and warn if no region
-    is specified. S3 buckets with dots cannot use virtual-hosted-style URLs,
-    which breaks automatic region detection.
-
-    See: https://github.com/lancedb/lancedb/issues/1898
-    """
-    if not isinstance(uri, str) or not uri.startswith("s3://"):
-        return
-
-    parsed = urlparse(uri)
-    bucket = parsed.netloc
-
-    if "." not in bucket:
-        return
-
-    # Check if region is provided in storage_options
-    region_keys = {"region", "aws_region"}
-    has_region = storage_options and any(k in storage_options for k in region_keys)
-
-    if not has_region:
-        raise ValueError(
-            f"S3 bucket name '{bucket}' contains dots, which prevents automatic "
-            f"region detection. Please specify the region explicitly via "
-            f"storage_options={{'region': '<your-region>'}} or "
-            f"storage_options={{'aws_region': '<your-region>'}}. "
-            f"See https://github.com/lancedb/lancedb/issues/1898 for details."
-        )
-
-
 def connect(
    uri: URI,
    *,
@@ -155,11 +121,9 @@ def connect(
            storage_options=storage_options,
            **kwargs,
        )
-    _check_s3_bucket_with_dots(str(uri), storage_options)

    if kwargs:
        raise ValueError(f"Unknown keyword arguments: {kwargs}")
-
    return LanceDBConnection(
        uri,
        read_consistency_interval=read_consistency_interval,
@@ -247,8 +211,6 @@ async def connect_async(
    if isinstance(client_config, dict):
        client_config = ClientConfig(**client_config)

-    _check_s3_bucket_with_dots(str(uri), storage_options)
-
    return AsyncConnection(
        await lancedb_connect(
            sanitize_uri(uri),
--- a/python/python/lancedb/embeddings/voyageai.py
+++ b/python/python/lancedb/embeddings/voyageai.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import base64
 import os
-from typing import ClassVar, TYPE_CHECKING, List, Union, Any, Generator, Optional
+from typing import ClassVar, TYPE_CHECKING, List, Union, Any, Generator

 from pathlib import Path
 from urllib.parse import urlparse
@@ -45,29 +45,11 @@ def is_valid_url(text):
        return False


-VIDEO_EXTENSIONS = {".mp4", ".webm", ".mov", ".avi", ".mkv", ".m4v", ".gif"}
-
-
-def is_video_url(url: str) -> bool:
-    """Check if URL points to a video file based on extension."""
-    parsed = urlparse(url)
-    path = parsed.path.lower()
-    return any(path.endswith(ext) for ext in VIDEO_EXTENSIONS)
-
-
-def is_video_path(path: Path) -> bool:
-    """Check if file path is a video file based on extension."""
-    return path.suffix.lower() in VIDEO_EXTENSIONS
-
-
 def transform_input(input_data: Union[str, bytes, Path]):
    PIL = attempt_import_or_raise("PIL", "pillow")
    if isinstance(input_data, str):
        if is_valid_url(input_data):
-            if is_video_url(input_data):
-                content = {"type": "video_url", "video_url": input_data}
-            else:
-                content = {"type": "image_url", "image_url": input_data}
+            content = {"type": "image_url", "image_url": input_data}
        else:
            content = {"type": "text", "text": input_data}
    elif isinstance(input_data, PIL.Image.Image):
@@ -88,24 +70,14 @@ def transform_input(input_data: Union[str, bytes, Path]):
            "image_base64": "data:image/jpeg;base64," + img_str,
        }
    elif isinstance(input_data, Path):
-        if is_video_path(input_data):
-            # Read video file and encode as base64
-            with open(input_data, "rb") as f:
-                video_bytes = f.read()
-            video_str = base64.b64encode(video_bytes).decode("utf-8")
-            content = {
-                "type": "video_base64",
-                "video_base64": video_str,
-            }
-        else:
-            img = PIL.Image.open(input_data)
-            buffered = BytesIO()
-            img.save(buffered, format="JPEG")
-            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-            content = {
-                "type": "image_base64",
-                "image_base64": "data:image/jpeg;base64," + img_str,
-            }
+        img = PIL.Image.open(input_data)
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        content = {
+            "type": "image_base64",
+            "image_base64": "data:image/jpeg;base64," + img_str,
+        }
    else:
        raise ValueError("Each input should be either str, bytes, Path or Image.")

@@ -119,8 +91,6 @@ def sanitize_multimodal_input(inputs: Union[TEXT, IMAGES]) -> List[Any]:
    PIL = attempt_import_or_raise("PIL", "pillow")
    if isinstance(inputs, (str, bytes, Path, PIL.Image.Image)):
        inputs = [inputs]
-    elif isinstance(inputs, list):
-        pass  # Already a list, use as-is
    elif isinstance(inputs, pa.Array):
        inputs = inputs.to_pylist()
    elif isinstance(inputs, pa.ChunkedArray):
@@ -173,16 +143,11 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
            * voyage-3
            * voyage-3-lite
            * voyage-multimodal-3
-            * voyage-multimodal-3.5
            * voyage-finance-2
            * voyage-multilingual-2
            * voyage-law-2
            * voyage-code-2

-    output_dimension: int, optional
-        The output dimension for models that support flexible dimensions.
-        Currently only voyage-multimodal-3.5 supports this feature.
-        Valid options: 256, 512, 1024 (default), 2048.

    Examples
    --------
@@ -210,10 +175,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
    """

    name: str
-    output_dimension: Optional[int] = None
    client: ClassVar = None
-    _FLEXIBLE_DIM_MODELS: ClassVar[list] = ["voyage-multimodal-3.5"]
-    _VALID_DIMENSIONS: ClassVar[list] = [256, 512, 1024, 2048]
    text_embedding_models: list = [
        "voyage-3.5",
        "voyage-3.5-lite",
@@ -224,7 +186,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
        "voyage-law-2",
        "voyage-code-2",
    ]
-    multimodal_embedding_models: list = ["voyage-multimodal-3", "voyage-multimodal-3.5"]
+    multimodal_embedding_models: list = ["voyage-multimodal-3"]
    contextual_embedding_models: list = ["voyage-context-3"]

    def _is_multimodal_model(self, model_name: str):
@@ -236,17 +198,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
        return model_name in self.contextual_embedding_models or "context" in model_name

    def ndims(self):
-        # Handle flexible dimension models
-        if self.name in self._FLEXIBLE_DIM_MODELS:
-            if self.output_dimension is not None:
-                if self.output_dimension not in self._VALID_DIMENSIONS:
-                    raise ValueError(
-                        f"Invalid output_dimension {self.output_dimension} "
-                        f"for {self.name}. Valid options: {self._VALID_DIMENSIONS}"
-                    )
-                return self.output_dimension
-            return 1024  # default dimension
-
        if self.name == "voyage-3-lite":
            return 512
        elif self.name == "voyage-code-2":
@@ -260,17 +211,12 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
            "voyage-finance-2",
            "voyage-multilingual-2",
            "voyage-law-2",
+            "voyage-multimodal-3",
        ]:
            return 1024
        else:
            raise ValueError(f"Model {self.name} not supported")

-    def _get_multimodal_kwargs(self, **kwargs):
-        """Get kwargs for multimodal embed call, including output_dimension if set."""
-        if self.name in self._FLEXIBLE_DIM_MODELS and self.output_dimension is not None:
-            kwargs["output_dimension"] = self.output_dimension
-        return kwargs
-
    def compute_query_embeddings(
        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
    ) -> List[np.ndarray]:
@@ -288,7 +234,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
        """
        client = VoyageAIEmbeddingFunction._get_client()
        if self._is_multimodal_model(self.name):
-            kwargs = self._get_multimodal_kwargs(**kwargs)
            result = client.multimodal_embed(
                inputs=[[query]], model=self.name, input_type="query", **kwargs
            )
@@ -330,7 +275,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
            )
            if has_images:
                # Use non-batched API for images
-                kwargs = self._get_multimodal_kwargs(**kwargs)
                result = client.multimodal_embed(
                    inputs=sanitized, model=self.name, input_type="document", **kwargs
                )
@@ -413,7 +357,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
            callable: A function that takes a batch of texts and returns embeddings.
        """
        if self._is_multimodal_model(self.name):
-            multimodal_kwargs = self._get_multimodal_kwargs(**kwargs)

            def embed_batch(batch: List[str]) -> List[np.array]:
                batch_inputs = sanitize_multimodal_input(batch)
@@ -421,7 +364,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
                    inputs=batch_inputs,
                    model=self.name,
                    input_type=input_type,
-                    **multimodal_kwargs,
+                    **kwargs,
                )
                return result.embeddings

--- a/python/python/lancedb/remote/db.py
+++ b/python/python/lancedb/remote/db.py
@@ -384,7 +384,6 @@ class RemoteDBConnection(DBConnection):
        on_bad_vectors: str = "error",
        fill_value: float = 0.0,
        mode: Optional[str] = None,
-        exist_ok: bool = False,
        embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
        *,
        namespace: Optional[List[str]] = None,
@@ -413,12 +412,6 @@ class RemoteDBConnection(DBConnection):
            - pyarrow.Schema

            - [LanceModel][lancedb.pydantic.LanceModel]
-        mode: str, default "create"
-            The mode to use when creating the table.
-            Can be either "create", "overwrite", or "exist_ok".
-        exist_ok: bool, default False
-            If exist_ok is True, and mode is None or "create", mode will be changed
-            to "exist_ok".
        on_bad_vectors: str, default "error"
            What to do if any of the vectors are not the same size or contains NaNs.
            One of "error", "drop", "fill".
@@ -490,11 +483,6 @@ class RemoteDBConnection(DBConnection):
        LanceTable(table4)

        """
-        if exist_ok:
-            if mode == "create":
-                mode = "exist_ok"
-            elif not mode:
-                mode = "exist_ok"
        if namespace is None:
            namespace = []
        validate_table_name(name)
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -18,17 +18,7 @@ from lancedb._lancedb import (
    UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.index import (
-    FTS,
-    BTree,
-    Bitmap,
-    HnswSq,
-    IvfFlat,
-    IvfPq,
-    IvfRq,
-    IvfSq,
-    LabelList,
-)
+from lancedb.index import FTS, BTree, Bitmap, HnswSq, IvfFlat, IvfPq, IvfSq, LabelList
 from lancedb.remote.db import LOOP
 import pyarrow as pa

@@ -275,12 +265,6 @@ class RemoteTable(Table):
                num_sub_vectors=num_sub_vectors,
                num_bits=num_bits,
            )
-        elif index_type == "IVF_RQ":
-            config = IvfRq(
-                distance_type=metric,
-                num_partitions=num_partitions,
-                num_bits=num_bits,
-            )
        elif index_type == "IVF_SQ":
            config = IvfSq(distance_type=metric, num_partitions=num_partitions)
        elif index_type == "IVF_HNSW_PQ":
@@ -295,8 +279,7 @@ class RemoteTable(Table):
        else:
            raise ValueError(
                f"Unknown vector index type: {index_type}. Valid options are"
-                " 'IVF_FLAT', 'IVF_PQ', 'IVF_RQ', 'IVF_SQ',"
-                " 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
+                " 'IVF_FLAT', 'IVF_SQ', 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
            )

        LOOP.run(
--- a/python/python/tests/test_embeddings_slow.py
+++ b/python/python/tests/test_embeddings_slow.py
@@ -613,133 +613,6 @@ def test_voyageai_multimodal_embedding_text_function():
    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()


-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_embedding_function():
-    """Test voyage-multimodal-3.5 model with text input."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", max_retries=0)
-    )
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table("test_multimodal_35", schema=TextModel, mode="overwrite")
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
-    assert voyageai.ndims() == 1024
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_flexible_dimensions():
-    """Test voyage-multimodal-3.5 model with custom output dimension."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", output_dimension=512, max_retries=0)
-    )
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    assert voyageai.ndims() == 512
-
-    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table("test_multimodal_35_dim", schema=TextModel, mode="overwrite")
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == 512
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_image_embedding():
-    """Test voyage-multimodal-3.5 model with image input."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", max_retries=0)
-    )
-
-    class Images(LanceModel):
-        label: str
-        image_uri: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    db = lancedb.connect("~/lancedb")
-    table = db.create_table(
-        "test_multimodal_35_images", schema=Images, mode="overwrite"
-    )
-    labels = ["cat", "dog"]
-    uris = [
-        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    ]
-    table.add(pd.DataFrame({"label": labels, "image_uri": uris}))
-    assert len(table.to_pandas()["vector"][0]) == voyageai.ndims()
-    assert voyageai.ndims() == 1024
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-@pytest.mark.parametrize("dimension", [256, 512, 1024, 2048])
-def test_voyageai_multimodal_35_all_dimensions(dimension):
-    """Test voyage-multimodal-3.5 model with all valid output dimensions."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", output_dimension=dimension, max_retries=0)
-    )
-
-    assert voyageai.ndims() == dimension
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    df = pd.DataFrame({"text": ["hello world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table(
-        f"test_multimodal_35_dim_{dimension}", schema=TextModel, mode="overwrite"
-    )
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == dimension
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_invalid_dimension():
-    """Test voyage-multimodal-3.5 model raises error for invalid output dimension."""
-    with pytest.raises(ValueError, match="Invalid output_dimension"):
-        voyageai = (
-            get_registry()
-            .get("voyageai")
-            .create(name="voyage-multimodal-3.5", output_dimension=999, max_retries=0)
-        )
-        # ndims() is where the validation happens
-        voyageai.ndims()
-
-
@pytest.mark.slow
@pytest.mark.skipif(
    importlib.util.find_spec("colpali_engine") is None,
--- a/python/python/tests/test_remote_db.py
+++ b/python/python/tests/test_remote_db.py
@@ -168,42 +168,6 @@ def test_table_len_sync():
        assert len(table) == 1


-def test_create_table_exist_ok():
-    def handler(request):
-        if request.path == "/v1/table/test/create/?mode=exist_ok":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b"{}")
-        else:
-            request.send_response(404)
-            request.end_headers()
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], exist_ok=True)
-        assert table is not None
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], mode="create", exist_ok=True)
-        assert table is not None
-
-
-def test_create_table_exist_ok_with_mode_overwrite():
-    def handler(request):
-        if request.path == "/v1/table/test/create/?mode=overwrite":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b"{}")
-        else:
-            request.send_response(404)
-            request.end_headers()
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], mode="overwrite", exist_ok=True)
-        assert table is not None
-
-
@pytest.mark.asyncio
 async def test_http_error():
    request_id_holder = {"request_id": None}
--- a/python/python/tests/test_s3_bucket_dots.py
+++ b/python/python/tests/test_s3_bucket_dots.py
@@ -1,68 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-"""
-Tests for S3 bucket names containing dots.
-
-Related issue: https://github.com/lancedb/lancedb/issues/1898
-
-These tests validate the early error checking for S3 bucket names with dots.
-No actual S3 connection is made - validation happens before connection.
-"""
-
-import pytest
-import lancedb
-
-# Test URIs
-BUCKET_WITH_DOTS = "s3://my.bucket.name/path"
-BUCKET_WITH_DOTS_AND_REGION = ("s3://my.bucket.name", {"region": "us-east-1"})
-BUCKET_WITH_DOTS_AND_AWS_REGION = ("s3://my.bucket.name", {"aws_region": "us-east-1"})
-BUCKET_WITHOUT_DOTS = "s3://my-bucket/path"
-
-
-class TestS3BucketWithDotsSync:
-    """Tests for connect()."""
-
-    def test_bucket_with_dots_requires_region(self):
-        with pytest.raises(ValueError, match="contains dots"):
-            lancedb.connect(BUCKET_WITH_DOTS)
-
-    def test_bucket_with_dots_and_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        db = lancedb.connect(uri, storage_options=opts)
-        assert db is not None
-
-    def test_bucket_with_dots_and_aws_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        db = lancedb.connect(uri, storage_options=opts)
-        assert db is not None
-
-    def test_bucket_without_dots_passes(self):
-        db = lancedb.connect(BUCKET_WITHOUT_DOTS)
-        assert db is not None
-
-
-class TestS3BucketWithDotsAsync:
-    """Tests for connect_async()."""
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_requires_region(self):
-        with pytest.raises(ValueError, match="contains dots"):
-            await lancedb.connect_async(BUCKET_WITH_DOTS)
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        db = await lancedb.connect_async(uri, storage_options=opts)
-        assert db is not None
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_aws_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        db = await lancedb.connect_async(uri, storage_options=opts)
-        assert db is not None
-
-    @pytest.mark.asyncio
-    async def test_bucket_without_dots_passes(self):
-        db = await lancedb.connect_async(BUCKET_WITHOUT_DOTS)
-        assert db is not None
--- a/python/src/arrow.rs
+++ b/python/src/arrow.rs
@@ -10,7 +10,8 @@ use arrow::{
 use futures::stream::StreamExt;
 use lancedb::arrow::SendableRecordBatchStream;
 use pyo3::{
-    exceptions::PyStopAsyncIteration, pyclass, pymethods, Bound, Py, PyAny, PyRef, PyResult, Python,
+    exceptions::PyStopAsyncIteration, pyclass, pymethods, Bound, PyAny, PyObject, PyRef, PyResult,
+    Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

@@ -35,11 +36,8 @@ impl RecordBatchStream {
 #[pymethods]
 impl RecordBatchStream {
    #[getter]
-    pub fn schema(&self, py: Python) -> PyResult<Py<PyAny>> {
-        (*self.schema)
-            .clone()
-            .into_pyarrow(py)
-            .map(|bound| bound.unbind())
+    pub fn schema(&self, py: Python) -> PyResult<PyObject> {
+        (*self.schema).clone().into_pyarrow(py)
    }

    pub fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
@@ -55,12 +53,7 @@ impl RecordBatchStream {
                .next()
                .await
                .ok_or_else(|| PyStopAsyncIteration::new_err(""))?;
-            Python::attach(|py| {
-                inner_next
-                    .infer_error()?
-                    .to_pyarrow(py)
-                    .map(|bound| bound.unbind())
-            })
+            Python::with_gil(|py| inner_next.infer_error()?.to_pyarrow(py))
        })
    }
 }
--- a/python/src/connection.rs
+++ b/python/src/connection.rs
@@ -12,7 +12,7 @@ use pyo3::{
    exceptions::{PyRuntimeError, PyValueError},
    pyclass, pyfunction, pymethods,
    types::{PyDict, PyDictMethods},
-    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
+    Bound, FromPyObject, Py, PyAny, PyObject, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

@@ -114,7 +114,7 @@ impl Connection {
        data: Bound<'_, PyAny>,
        namespace: Vec<String>,
        storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
        location: Option<String>,
    ) -> PyResult<Bound<'a, PyAny>> {
        let inner = self_.get_inner()?.clone();
@@ -152,7 +152,7 @@ impl Connection {
        schema: Bound<'_, PyAny>,
        namespace: Vec<String>,
        storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
        location: Option<String>,
    ) -> PyResult<Bound<'a, PyAny>> {
        let inner = self_.get_inner()?.clone();
@@ -187,7 +187,7 @@ impl Connection {
        name: String,
        namespace: Vec<String>,
        storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
        index_cache_size: Option<u32>,
        location: Option<String>,
    ) -> PyResult<Bound<'_, PyAny>> {
@@ -304,7 +304,6 @@ impl Connection {
                },
                page_token,
                limit: limit.map(|l| l as i32),
-                ..Default::default()
            };
            let response = inner.list_namespaces(request).await.infer_error()?;
            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
@@ -326,12 +325,12 @@ impl Connection {
        let inner = self_.get_inner()?.clone();
        let py = self_.py();
        future_into_py(py, async move {
-            use lance_namespace::models::CreateNamespaceRequest;
-            let mode_enum = mode.map(|m| match m.to_lowercase().as_str() {
-                "create" => "Create".to_string(),
-                "exist_ok" => "ExistOk".to_string(),
-                "overwrite" => "Overwrite".to_string(),
-                other => other.to_string(),
+            use lance_namespace::models::{create_namespace_request, CreateNamespaceRequest};
+            let mode_enum = mode.and_then(|m| match m.to_lowercase().as_str() {
+                "create" => Some(create_namespace_request::Mode::Create),
+                "exist_ok" => Some(create_namespace_request::Mode::ExistOk),
+                "overwrite" => Some(create_namespace_request::Mode::Overwrite),
+                _ => None,
            });
            let request = CreateNamespaceRequest {
                id: if namespace.is_empty() {
@@ -341,7 +340,6 @@ impl Connection {
                },
                mode: mode_enum,
                properties,
-                ..Default::default()
            };
            let response = inner.create_namespace(request).await.infer_error()?;
            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
@@ -362,16 +360,16 @@ impl Connection {
        let inner = self_.get_inner()?.clone();
        let py = self_.py();
        future_into_py(py, async move {
-            use lance_namespace::models::DropNamespaceRequest;
-            let mode_enum = mode.map(|m| match m.to_uppercase().as_str() {
-                "SKIP" => "Skip".to_string(),
-                "FAIL" => "Fail".to_string(),
-                other => other.to_string(),
+            use lance_namespace::models::{drop_namespace_request, DropNamespaceRequest};
+            let mode_enum = mode.and_then(|m| match m.to_uppercase().as_str() {
+                "SKIP" => Some(drop_namespace_request::Mode::Skip),
+                "FAIL" => Some(drop_namespace_request::Mode::Fail),
+                _ => None,
            });
-            let behavior_enum = behavior.map(|b| match b.to_uppercase().as_str() {
-                "RESTRICT" => "Restrict".to_string(),
-                "CASCADE" => "Cascade".to_string(),
-                other => other.to_string(),
+            let behavior_enum = behavior.and_then(|b| match b.to_uppercase().as_str() {
+                "RESTRICT" => Some(drop_namespace_request::Behavior::Restrict),
+                "CASCADE" => Some(drop_namespace_request::Behavior::Cascade),
+                _ => None,
            });
            let request = DropNamespaceRequest {
                id: if namespace.is_empty() {
@@ -381,7 +379,6 @@ impl Connection {
                },
                mode: mode_enum,
                behavior: behavior_enum,
-                ..Default::default()
            };
            let response = inner.drop_namespace(request).await.infer_error()?;
            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
@@ -408,7 +405,6 @@ impl Connection {
                } else {
                    Some(namespace)
                },
-                ..Default::default()
            };
            let response = inner.describe_namespace(request).await.infer_error()?;
            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
@@ -438,7 +434,6 @@ impl Connection {
                },
                page_token,
                limit: limit.map(|l| l as i32),
-                ..Default::default()
            };
            let response = inner.list_tables(request).await.infer_error()?;
            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
--- a/python/src/lib.rs
+++ b/python/src/lib.rs
@@ -1,4 +1,3 @@
-#![allow(deprecated)]
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

--- a/python/src/permutation.rs
+++ b/python/src/permutation.rs
@@ -281,7 +281,7 @@ impl PyPermutationReader {
        let reader = slf.reader.clone();
        future_into_py(slf.py(), async move {
            let schema = reader.output_schema(selection).await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

--- a/python/src/query.rs
+++ b/python/src/query.rs
@@ -216,7 +216,7 @@ impl<'py> IntoPyObject<'py> for PyQueryVectors {
        let py_objs = self
            .0
            .into_iter()
-            .map(|v| v.to_data().into_pyarrow(py).map(|bound| bound.unbind()))
+            .map(|v| v.to_data().into_pyarrow(py))
            .collect::<Result<Vec<_>, _>>()?;
        PyList::new(py, py_objs)
    }
@@ -453,7 +453,7 @@ impl Query {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            let schema = inner.output_schema().await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

@@ -532,7 +532,7 @@ impl TakeQuery {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            let schema = inner.output_schema().await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

@@ -627,7 +627,7 @@ impl FTSQuery {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            let schema = inner.output_schema().await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

@@ -806,7 +806,7 @@ impl VectorQuery {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            let schema = inner.output_schema().await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

--- a/python/src/storage_options.rs
+++ b/python/src/storage_options.rs
@@ -17,7 +17,7 @@ use pyo3::types::PyDict;
 /// Internal wrapper around a Python object implementing StorageOptionsProvider
 pub struct PyStorageOptionsProvider {
    /// The Python object implementing fetch_storage_options()
-    inner: Py<PyAny>,
+    inner: PyObject,
 }

 impl Clone for PyStorageOptionsProvider {
@@ -29,7 +29,7 @@ impl Clone for PyStorageOptionsProvider {
 }

 impl PyStorageOptionsProvider {
-    pub fn new(obj: Py<PyAny>) -> PyResult<Self> {
+    pub fn new(obj: PyObject) -> PyResult<Self> {
        Python::with_gil(|py| {
            // Verify the object has a fetch_storage_options method
            if !obj.bind(py).hasattr("fetch_storage_options")? {
@@ -143,7 +143,7 @@ impl std::fmt::Debug for PyStorageOptionsProviderWrapper {
 /// This is the main entry point for converting Python StorageOptionsProvider objects
 /// to Rust trait objects that can be used by the Lance ecosystem.
 pub fn py_object_to_storage_options_provider(
-    py_obj: Py<PyAny>,
+    py_obj: PyObject,
 ) -> PyResult<Arc<dyn StorageOptionsProvider>> {
    let py_provider = PyStorageOptionsProvider::new(py_obj)?;
    Ok(Arc::new(PyStorageOptionsProviderWrapper::new(py_provider)))
--- a/python/src/table.rs
+++ b/python/src/table.rs
@@ -287,7 +287,7 @@ impl Table {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let schema = inner.schema().await.infer_error()?;
-            Python::with_gil(|py| schema.to_pyarrow(py).map(|bound| bound.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
        })
    }

--- a/rust/lancedb/src/connection.rs
+++ b/rust/lancedb/src/connection.rs
@@ -1325,27 +1325,25 @@ mod tests {

    #[tokio::test]
    async fn test_table_names() {
-        let tc = new_test_connection().await.unwrap();
-        let db = tc.connection;
-        let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
+        let tmp_dir = tempdir().unwrap();
        let mut names = Vec::with_capacity(100);
        for _ in 0..100 {
-            let name = uuid::Uuid::new_v4().to_string();
+            let mut name = uuid::Uuid::new_v4().to_string();
            names.push(name.clone());
-            db.create_empty_table(name, schema.clone())
-                .execute()
-                .await
-                .unwrap();
+            name.push_str(".lance");
+            create_dir_all(tmp_dir.path().join(&name)).unwrap();
        }
        names.sort();
-        let tables = db.table_names().limit(100).execute().await.unwrap();
+
+        let uri = tmp_dir.path().to_str().unwrap();
+        let db = connect(uri).execute().await.unwrap();
+        let tables = db.table_names().execute().await.unwrap();

        assert_eq!(tables, names);

        let tables = db
            .table_names()
            .start_after(&names[30])
-            .limit(100)
            .execute()
            .await
            .unwrap();
--- a/rust/lancedb/src/database/namespace.rs
+++ b/rust/lancedb/src/database/namespace.rs
@@ -9,7 +9,7 @@ use std::sync::Arc;
 use async_trait::async_trait;
 use lance_namespace::{
    models::{
-        CreateNamespaceRequest, CreateNamespaceResponse, DeclareTableRequest,
+        CreateEmptyTableRequest, CreateNamespaceRequest, CreateNamespaceResponse,
        DescribeNamespaceRequest, DescribeNamespaceResponse, DescribeTableRequest,
        DropNamespaceRequest, DropNamespaceResponse, DropTableRequest, ListNamespacesRequest,
        ListNamespacesResponse, ListTablesRequest, ListTablesResponse,
@@ -137,7 +137,6 @@ impl Database for LanceNamespaceDatabase {
            id: Some(request.namespace),
            page_token: request.start_after,
            limit: request.limit.map(|l| l as i32),
-            ..Default::default()
        };

        let response = self.namespace.list_tables(ns_request).await?;
@@ -155,7 +154,6 @@ impl Database for LanceNamespaceDatabase {
        let describe_request = DescribeTableRequest {
            id: Some(table_id.clone()),
            version: None,
-            ..Default::default()
        };

        let describe_result = self.namespace.describe_table(describe_request).await;
@@ -173,7 +171,6 @@ impl Database for LanceNamespaceDatabase {
                    // Drop the existing table - must succeed
                    let drop_request = DropTableRequest {
                        id: Some(table_id.clone()),
-                        ..Default::default()
                    };
                    self.namespace
                        .drop_table(drop_request)
@@ -205,24 +202,29 @@ impl Database for LanceNamespaceDatabase {
        let mut table_id = request.namespace.clone();
        table_id.push(request.name.clone());

-        let declare_request = DeclareTableRequest {
+        let create_empty_request = CreateEmptyTableRequest {
            id: Some(table_id.clone()),
            location: None,
-            vend_credentials: None,
-            ..Default::default()
+            properties: if self.storage_options.is_empty() {
+                None
+            } else {
+                Some(self.storage_options.clone())
+            },
        };

-        let declare_response = self
+        let create_empty_response = self
            .namespace
-            .declare_table(declare_request)
+            .create_empty_table(create_empty_request)
            .await
            .map_err(|e| Error::Runtime {
-                message: format!("Failed to declare table: {}", e),
+                message: format!("Failed to create empty table: {}", e),
            })?;

-        let location = declare_response.location.ok_or_else(|| Error::Runtime {
-            message: "Table location is missing from declare_table response".to_string(),
-        })?;
+        let location = create_empty_response
+            .location
+            .ok_or_else(|| Error::Runtime {
+                message: "Table location is missing from create_empty_table response".to_string(),
+            })?;

        let native_table = NativeTable::create_from_namespace(
            self.namespace.clone(),
@@ -279,10 +281,7 @@ impl Database for LanceNamespaceDatabase {
        let mut table_id = namespace.to_vec();
        table_id.push(name.to_string());

-        let drop_request = DropTableRequest {
-            id: Some(table_id),
-            ..Default::default()
-        };
+        let drop_request = DropTableRequest { id: Some(table_id) };
        self.namespace
            .drop_table(drop_request)
            .await
@@ -439,7 +438,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -501,7 +499,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -566,7 +563,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -651,7 +647,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -708,7 +703,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -790,7 +784,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
@@ -825,7 +818,6 @@ mod tests {
            id: Some(vec!["test_ns".into()]),
            mode: None,
            properties: None,
-            ..Default::default()
        })
        .await
        .expect("Failed to create namespace");
--- a/rust/lancedb/src/embeddings.rs
+++ b/rust/lancedb/src/embeddings.rs
@@ -120,13 +120,8 @@ impl MemoryRegistry {
 }

 /// A record batch reader that has embeddings applied to it
-///
-/// This is a wrapper around another record batch reader that applies embedding functions
-/// when reading from the record batch.
-///
-/// When multiple embedding functions are defined, they are computed in parallel using
-/// scoped threads to improve performance. For a single embedding function, computation
-/// is done inline without threading overhead.
+/// This is a wrapper around another record batch reader that applies an embedding function
+/// when reading from the record batch
 pub struct WithEmbeddings<R: RecordBatchReader> {
    inner: R,
    embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
@@ -240,48 +235,6 @@ impl<R: RecordBatchReader> WithEmbeddings<R> {
            column_definitions,
        })
    }
-
-    fn compute_embeddings_parallel(&self, batch: &RecordBatch) -> Result<Vec<Arc<dyn Array>>> {
-        if self.embeddings.len() == 1 {
-            let (fld, func) = &self.embeddings[0];
-            let src_column =
-                batch
-                    .column_by_name(&fld.source_column)
-                    .ok_or_else(|| Error::InvalidInput {
-                        message: format!("Source column '{}' not found", fld.source_column),
-                    })?;
-            return Ok(vec![func.compute_source_embeddings(src_column.clone())?]);
-        }
-
-        // Parallel path: multiple embeddings
-        std::thread::scope(|s| {
-            let handles: Vec<_> = self
-                .embeddings
-                .iter()
-                .map(|(fld, func)| {
-                    let src_column = batch.column_by_name(&fld.source_column).ok_or_else(|| {
-                        Error::InvalidInput {
-                            message: format!("Source column '{}' not found", fld.source_column),
-                        }
-                    })?;
-
-                    let handle =
-                        s.spawn(move || func.compute_source_embeddings(src_column.clone()));
-
-                    Ok(handle)
-                })
-                .collect::<Result<_>>()?;
-
-            handles
-                .into_iter()
-                .map(|h| {
-                    h.join().map_err(|e| Error::Runtime {
-                        message: format!("Thread panicked during embedding computation: {:?}", e),
-                    })?
-                })
-                .collect()
-        })
-    }
 }

 impl<R: RecordBatchReader> Iterator for MaybeEmbedded<R> {
@@ -309,19 +262,19 @@ impl<R: RecordBatchReader> Iterator for WithEmbeddings<R> {
    fn next(&mut self) -> Option<Self::Item> {
        let batch = self.inner.next()?;
        match batch {
-            Ok(batch) => {
-                let embeddings = match self.compute_embeddings_parallel(&batch) {
-                    Ok(emb) => emb,
-                    Err(e) => {
-                        return Some(Err(arrow_schema::ArrowError::ComputeError(format!(
-                            "Error computing embedding: {}",
-                            e
-                        ))))
-                    }
-                };
-
-                let mut batch = batch;
-                for ((fld, _), embedding) in self.embeddings.iter().zip(embeddings.iter()) {
+            Ok(mut batch) => {
+                // todo: parallelize this
+                for (fld, func) in self.embeddings.iter() {
+                    let src_column = batch.column_by_name(&fld.source_column).unwrap();
+                    let embedding = match func.compute_source_embeddings(src_column.clone()) {
+                        Ok(embedding) => embedding,
+                        Err(e) => {
+                            return Some(Err(arrow_schema::ArrowError::ComputeError(format!(
+                                "Error computing embedding: {}",
+                                e
+                            ))))
+                        }
+                    };
                    let dst_field_name = fld
                        .dest_column
                        .clone()
@@ -333,7 +286,7 @@ impl<R: RecordBatchReader> Iterator for WithEmbeddings<R> {
                        embedding.nulls().is_some(),
                    );

-                    match batch.try_with_column(dst_field.clone(), embedding.clone()) {
+                    match batch.try_with_column(dst_field.clone(), embedding) {
                        Ok(b) => batch = b,
                        Err(e) => return Some(Err(e)),
                    };
--- a/rust/lancedb/src/remote/db.rs
+++ b/rust/lancedb/src/remote/db.rs
@@ -1720,7 +1720,6 @@ mod tests {
                id: Some(namespace.clone()),
                mode: None,
                properties: None,
-                ..Default::default()
            })
            .await
            .expect("Failed to create namespace");
@@ -1747,7 +1746,6 @@ mod tests {
                    id: Some(namespace.clone()),
                    page_token: None,
                    limit: None,
-                    ..Default::default()
                })
                .await
                .expect("Failed to list tables");
@@ -1760,7 +1758,6 @@ mod tests {
                    id: Some(namespace.clone()),
                    page_token: None,
                    limit: None,
-                    ..Default::default()
                })
                .await
                .unwrap();
@@ -1802,7 +1799,6 @@ mod tests {
                id: Some(namespace.clone()),
                mode: None,
                properties: None,
-                ..Default::default()
            })
            .await
            .expect("Failed to create namespace");
@@ -1829,7 +1825,6 @@ mod tests {
                    id: Some(namespace.clone()),
                    page_token: None,
                    limit: None,
-                    ..Default::default()
                })
                .await
                .unwrap();
--- a/rust/lancedb/src/remote/table.rs
+++ b/rust/lancedb/src/remote/table.rs
@@ -1088,17 +1088,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
                    body["num_partitions"] = serde_json::Value::Number(num_partitions.into());
                }
            }
-            Index::IvfRq(index) => {
-                body[INDEX_TYPE_KEY] = serde_json::Value::String("IVF_RQ".to_string());
-                body[METRIC_TYPE_KEY] =
-                    serde_json::Value::String(index.distance_type.to_string().to_lowercase());
-                if let Some(num_partitions) = index.num_partitions {
-                    body["num_partitions"] = serde_json::Value::Number(num_partitions.into());
-                }
-                if let Some(num_bits) = index.num_bits {
-                    body["num_bits"] = serde_json::Value::Number(num_bits.into());
-                }
-            }
            Index::BTree(_) => {
                body[INDEX_TYPE_KEY] = serde_json::Value::String("BTREE".to_string());
            }
--- a/rust/lancedb/src/table.rs
+++ b/rust/lancedb/src/table.rs
@@ -42,8 +42,8 @@ use lance_index::DatasetIndexExt;
 use lance_index::IndexType;
 use lance_io::object_store::LanceNamespaceStorageOptionsProvider;
 use lance_namespace::models::{
-    QueryTableRequest as NsQueryTableRequest, QueryTableRequestColumns,
-    QueryTableRequestFullTextQuery, QueryTableRequestVector, StringFtsQuery,
+    QueryTableRequest as NsQueryTableRequest, QueryTableRequestFullTextQuery,
+    QueryTableRequestVector, StringFtsQuery,
 };
 use lance_namespace::LanceNamespace;
 use lance_table::format::Manifest;
@@ -1424,7 +1424,7 @@ impl Table {
            })
            .collect::<Vec<_>>();

-        let unioned = UnionExec::try_new(projected_plans).unwrap();
+        let unioned = Arc::new(UnionExec::new(projected_plans));
        // We require 1 partition in the final output
        let repartitioned = RepartitionExec::try_new(
            unioned,
@@ -2332,18 +2332,6 @@ impl NativeTable {
        }
    }

-    /// Convert selected columns into the namespace request format.
-    fn namespace_columns(&self, columns: &[String]) -> Option<Box<QueryTableRequestColumns>> {
-        if columns.is_empty() {
-            None
-        } else {
-            Some(Box::new(QueryTableRequestColumns {
-                column_names: Some(columns.to_vec()),
-                column_aliases: None,
-            }))
-        }
-    }
-
    /// Convert an AnyQuery to the namespace QueryTableRequest format.
    fn convert_to_namespace_query(&self, query: &AnyQuery) -> Result<NsQueryTableRequest> {
        match query {
@@ -2360,7 +2348,7 @@ impl NativeTable {
                // Convert select to columns list
                let columns = match &vq.base.select {
                    Select::All => None,
-                    Select::Columns(cols) => self.namespace_columns(cols),
+                    Select::Columns(cols) => Some(cols.clone()),
                    Select::Dynamic(_) => {
                        return Err(Error::NotSupported {
                            message:
@@ -2395,8 +2383,6 @@ impl NativeTable {
                });

                Ok(NsQueryTableRequest {
-                    identity: None,
-                    context: None,
                    id: None, // Will be set in namespace_query
                    k: vq.base.limit.unwrap_or(10) as i32,
                    vector: Box::new(vector),
@@ -2435,7 +2421,7 @@ impl NativeTable {

                let columns = match &q.select {
                    Select::All => None,
-                    Select::Columns(cols) => self.namespace_columns(cols),
+                    Select::Columns(cols) => Some(cols.clone()),
                    Select::Dynamic(_) => {
                        return Err(Error::NotSupported {
                            message: "Dynamic columns are not supported for server-side query"
@@ -2467,8 +2453,6 @@ impl NativeTable {
                });

                Ok(NsQueryTableRequest {
-                    identity: None,
-                    context: None,
                    id: None, // Will be set by caller
                    vector,
                    k: q.limit.unwrap_or(10) as i32,
@@ -5162,13 +5146,7 @@ mod tests {
        assert_eq!(ns_request.k, 10);
        assert_eq!(ns_request.offset, Some(5));
        assert_eq!(ns_request.filter, Some("id > 0".to_string()));
-        let column_names = ns_request
-            .columns
-            .as_ref()
-            .and_then(|c| c.column_names.as_ref())
-            .cloned()
-            .unwrap();
-        assert_eq!(column_names, vec!["id".to_string()]);
+        assert_eq!(ns_request.columns, Some(vec!["id".to_string()]));
        assert_eq!(ns_request.vector_column, Some("vector".to_string()));
        assert_eq!(ns_request.distance_type, Some("l2".to_string()));
        assert!(ns_request.vector.single_vector.is_some());
@@ -5209,13 +5187,7 @@ mod tests {
        assert_eq!(ns_request.k, 20);
        assert_eq!(ns_request.offset, Some(5));
        assert_eq!(ns_request.filter, Some("id > 5".to_string()));
-        let column_names = ns_request
-            .columns
-            .as_ref()
-            .and_then(|c| c.column_names.as_ref())
-            .cloned()
-            .unwrap();
-        assert_eq!(column_names, vec!["id".to_string()]);
+        assert_eq!(ns_request.columns, Some(vec!["id".to_string()]));
        assert_eq!(ns_request.with_row_id, Some(true));
        assert_eq!(ns_request.bypass_vector_index, Some(true));
        assert!(ns_request.vector_column.is_none()); // No vector column for plain queries
--- a/rust/lancedb/src/table/dataset.rs
+++ b/rust/lancedb/src/table/dataset.rs
@@ -100,8 +100,7 @@ impl DatasetRef {
                let should_checkout = match &target_ref {
                    refs::Ref::Version(_, Some(target_ver)) => version != target_ver,
                    refs::Ref::Version(_, None) => true, // No specific version, always checkout
-                    refs::Ref::VersionNumber(target_ver) => version != target_ver,
-                    refs::Ref::Tag(_) => true, // Always checkout for tags
+                    refs::Ref::Tag(_) => true,           // Always checkout for tags
                };

                if should_checkout {
--- a/rust/lancedb/src/test_utils/connection.rs
+++ b/rust/lancedb/src/test_utils/connection.rs
@@ -5,19 +5,16 @@

 use regex::Regex;
 use std::env;
-use std::process::Stdio;
-use tokio::io::{AsyncBufReadExt, BufReader};
-use tokio::process::{Child, ChildStdout, Command};
-use tokio::sync::mpsc;
+use std::io::{BufRead, BufReader};
+use std::process::{Child, ChildStdout, Command, Stdio};

 use crate::{connect, Connection};
-use anyhow::{anyhow, bail, Result};
+use anyhow::{bail, Result};
 use tempfile::{tempdir, TempDir};

 pub struct TestConnection {
    pub uri: String,
    pub connection: Connection,
-    pub is_remote: bool,
    _temp_dir: Option<TempDir>,
    _process: Option<TestProcess>,
 }
@@ -40,56 +37,6 @@ pub async fn new_test_connection() -> Result<TestConnection> {
    }
 }

-async fn spawn_stdout_reader(
-    mut stdout: BufReader<ChildStdout>,
-    port_sender: mpsc::Sender<anyhow::Result<String>>,
-) -> tokio::task::JoinHandle<()> {
-    let print_stdout = env::var("PRINT_LANCEDB_TEST_CONNECTION_SCRIPT_OUTPUT").is_ok();
-    tokio::spawn(async move {
-        let mut line = String::new();
-        let re = Regex::new(r"Query node now listening on 0.0.0.0:(.*)").unwrap();
-        loop {
-            line.clear();
-            let result = stdout.read_line(&mut line).await;
-            if let Err(err) = result {
-                port_sender
-                    .send(Err(anyhow!(
-                        "error while reading from process output: {}",
-                        err
-                    )))
-                    .await
-                    .unwrap();
-                return;
-            } else if result.unwrap() == 0 {
-                port_sender
-                    .send(Err(anyhow!(
-                        " hit EOF before reading port from process output."
-                    )))
-                    .await
-                    .unwrap();
-                return;
-            }
-            if re.is_match(&line) {
-                let caps = re.captures(&line).unwrap();
-                port_sender.send(Ok(caps[1].to_string())).await.unwrap();
-                break;
-            }
-        }
-        loop {
-            line.clear();
-            match stdout.read_line(&mut line).await {
-                Err(_) => return,
-                Ok(0) => return,
-                Ok(_size) => {
-                    if print_stdout {
-                        print!("{}", line);
-                    }
-                }
-            }
-        }
-    })
-}
-
 async fn new_remote_connection(script_path: &str) -> Result<TestConnection> {
    let temp_dir = tempdir()?;
    let data_path = temp_dir.path().to_str().unwrap().to_string();
@@ -110,25 +57,38 @@ async fn new_remote_connection(script_path: &str) -> Result<TestConnection> {
        child: child_result.unwrap(),
    };
    let stdout = BufReader::new(process.child.stdout.take().unwrap());
-    let (port_sender, mut port_receiver) = mpsc::channel(5);
-    let _reader = spawn_stdout_reader(stdout, port_sender).await;
-    let port = match port_receiver.recv().await {
-        None => bail!("Unable to determine the port number used by the phalanx process we spawned, because the reader thread was closed too soon."),
-        Some(Err(err)) => bail!("Unable to determine the port number used by the phalanx process we spawned, because of an error, {}", err),
-        Some(Ok(port)) => port,
-    };
+    let port = read_process_port(stdout)?;
    let uri = "db://test";
    let host_override = format!("http://localhost:{}", port);
    let connection = create_new_connection(uri, &host_override).await?;
    Ok(TestConnection {
        uri: uri.to_string(),
        connection,
-        is_remote: true,
        _temp_dir: Some(temp_dir),
        _process: Some(process),
    })
 }

+fn read_process_port(mut stdout: BufReader<ChildStdout>) -> Result<String> {
+    let mut line = String::new();
+    let re = Regex::new(r"Query node now listening on 0.0.0.0:(.*)").unwrap();
+    loop {
+        let result = stdout.read_line(&mut line);
+        if let Err(err) = result {
+            bail!(format!(
+                "read_process_port: error while reading from process output: {}",
+                err
+            ));
+        } else if result.unwrap() == 0 {
+            bail!("read_process_port: hit EOF before reading port from process output.");
+        }
+        if re.is_match(&line) {
+            let caps = re.captures(&line).unwrap();
+            return Ok(caps[1].to_string());
+        }
+    }
+}
+
 #[cfg(feature = "remote")]
 async fn create_new_connection(uri: &str, host_override: &str) -> crate::error::Result<Connection> {
    connect(uri)
@@ -154,7 +114,6 @@ async fn new_local_connection() -> Result<TestConnection> {
    Ok(TestConnection {
        uri: uri.to_string(),
        connection,
-        is_remote: false,
        _temp_dir: Some(temp_dir),
        _process: None,
    })
--- a/rust/lancedb/tests/embeddings_parallel_test.rs
+++ b/rust/lancedb/tests/embeddings_parallel_test.rs
@@ -1,253 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-use std::{
-    borrow::Cow,
-    sync::{
-        atomic::{AtomicUsize, Ordering},
-        Arc,
-    },
-    time::Duration,
-};
-
-use arrow::buffer::NullBuffer;
-use arrow_array::{
-    Array, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator, StringArray,
-};
-use arrow_schema::{DataType, Field, Schema};
-use lancedb::{
-    embeddings::{EmbeddingDefinition, EmbeddingFunction, MaybeEmbedded, WithEmbeddings},
-    Error, Result,
-};
-
-#[derive(Debug)]
-struct SlowMockEmbed {
-    name: String,
-    dim: usize,
-    delay_ms: u64,
-    call_count: Arc<AtomicUsize>,
-}
-
-impl SlowMockEmbed {
-    pub fn new(name: String, dim: usize, delay_ms: u64) -> Self {
-        Self {
-            name,
-            dim,
-            delay_ms,
-            call_count: Arc::new(AtomicUsize::new(0)),
-        }
-    }
-
-    pub fn get_call_count(&self) -> usize {
-        self.call_count.load(Ordering::SeqCst)
-    }
-}
-
-impl EmbeddingFunction for SlowMockEmbed {
-    fn name(&self) -> &str {
-        &self.name
-    }
-
-    fn source_type(&self) -> Result<Cow<'_, DataType>> {
-        Ok(Cow::Owned(DataType::Utf8))
-    }
-
-    fn dest_type(&self) -> Result<Cow<'_, DataType>> {
-        Ok(Cow::Owned(DataType::new_fixed_size_list(
-            DataType::Float32,
-            self.dim as _,
-            true,
-        )))
-    }
-
-    fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-        // Simulate slow embedding computation
-        std::thread::sleep(Duration::from_millis(self.delay_ms));
-        self.call_count.fetch_add(1, Ordering::SeqCst);
-
-        let len = source.len();
-        let inner = Arc::new(Float32Array::from(vec![Some(1.0); len * self.dim]));
-        let field = Field::new("item", inner.data_type().clone(), false);
-        let arr = FixedSizeListArray::new(
-            Arc::new(field),
-            self.dim as _,
-            inner,
-            Some(NullBuffer::new_valid(len)),
-        );
-
-        Ok(Arc::new(arr))
-    }
-
-    fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-        unimplemented!()
-    }
-}
-
-fn create_test_batch() -> Result<RecordBatch> {
-    let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
-    let text = StringArray::from(vec!["hello", "world"]);
-    RecordBatch::try_new(schema, vec![Arc::new(text)]).map_err(|e| Error::Runtime {
-        message: format!("Failed to create test batch: {}", e),
-    })
-}
-
-#[test]
-fn test_single_embedding_fast_path() {
-    // Single embedding should execute without spawning threads
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed = Arc::new(SlowMockEmbed::new("test".to_string(), 2, 10));
-    let embedding_def = EmbeddingDefinition::new("text", "test", Some("embedding"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![(embedding_def, embed.clone() as Arc<dyn EmbeddingFunction>)];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-    assert!(result.column_by_name("embedding").is_some());
-    assert_eq!(embed.get_call_count(), 1);
-}
-
-#[test]
-fn test_multiple_embeddings_parallel() {
-    // Multiple embeddings should execute in parallel
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed1 = Arc::new(SlowMockEmbed::new("embed1".to_string(), 2, 100));
-    let embed2 = Arc::new(SlowMockEmbed::new("embed2".to_string(), 3, 100));
-    let embed3 = Arc::new(SlowMockEmbed::new("embed3".to_string(), 4, 100));
-
-    let def1 = EmbeddingDefinition::new("text", "embed1", Some("emb1"));
-    let def2 = EmbeddingDefinition::new("text", "embed2", Some("emb2"));
-    let def3 = EmbeddingDefinition::new("text", "embed3", Some("emb3"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![
-        (def1, embed1.clone() as Arc<dyn EmbeddingFunction>),
-        (def2, embed2.clone() as Arc<dyn EmbeddingFunction>),
-        (def3, embed3.clone() as Arc<dyn EmbeddingFunction>),
-    ];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-
-    // Verify all embedding columns are present
-    assert!(result.column_by_name("emb1").is_some());
-    assert!(result.column_by_name("emb2").is_some());
-    assert!(result.column_by_name("emb3").is_some());
-
-    // Verify all embeddings were computed
-    assert_eq!(embed1.get_call_count(), 1);
-    assert_eq!(embed2.get_call_count(), 1);
-    assert_eq!(embed3.get_call_count(), 1);
-}
-
-#[test]
-fn test_embedding_column_order_preserved() {
-    // Verify that embedding columns are added in the same order as definitions
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed1 = Arc::new(SlowMockEmbed::new("embed1".to_string(), 2, 10));
-    let embed2 = Arc::new(SlowMockEmbed::new("embed2".to_string(), 3, 10));
-    let embed3 = Arc::new(SlowMockEmbed::new("embed3".to_string(), 4, 10));
-
-    let def1 = EmbeddingDefinition::new("text", "embed1", Some("first"));
-    let def2 = EmbeddingDefinition::new("text", "embed2", Some("second"));
-    let def3 = EmbeddingDefinition::new("text", "embed3", Some("third"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![
-        (def1, embed1 as Arc<dyn EmbeddingFunction>),
-        (def2, embed2 as Arc<dyn EmbeddingFunction>),
-        (def3, embed3 as Arc<dyn EmbeddingFunction>),
-    ];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-    let result_schema = result.schema();
-
-    // Original column is first
-    assert_eq!(result_schema.field(0).name(), "text");
-    // Embedding columns follow in order
-    assert_eq!(result_schema.field(1).name(), "first");
-    assert_eq!(result_schema.field(2).name(), "second");
-    assert_eq!(result_schema.field(3).name(), "third");
-}
-
-#[test]
-fn test_embedding_error_propagation() {
-    // Test that errors from embedding computation are properly propagated
-    #[derive(Debug)]
-    struct FailingEmbed {
-        name: String,
-    }
-
-    impl EmbeddingFunction for FailingEmbed {
-        fn name(&self) -> &str {
-            &self.name
-        }
-
-        fn source_type(&self) -> Result<Cow<'_, DataType>> {
-            Ok(Cow::Owned(DataType::Utf8))
-        }
-
-        fn dest_type(&self) -> Result<Cow<'_, DataType>> {
-            Ok(Cow::Owned(DataType::new_fixed_size_list(
-                DataType::Float32,
-                2,
-                true,
-            )))
-        }
-
-        fn compute_source_embeddings(&self, _source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-            Err(Error::Runtime {
-                message: "Intentional failure".to_string(),
-            })
-        }
-
-        fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-            unimplemented!()
-        }
-    }
-
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed = Arc::new(FailingEmbed {
-        name: "failing".to_string(),
-    });
-    let def = EmbeddingDefinition::new("text", "failing", Some("emb"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![(def, embed as Arc<dyn EmbeddingFunction>)];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap();
-    assert!(result.is_err());
-    let err_msg = format!("{}", result.err().unwrap());
-    assert!(err_msg.contains("Intentional failure"));
-}
-
-#[test]
-fn test_maybe_embedded_with_no_embeddings() {
-    // Test that MaybeEmbedded::No variant works correctly
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch.clone())], schema.clone());
-    let table_def = lancedb::table::TableDefinition {
-        schema: schema.clone(),
-        column_definitions: vec![lancedb::table::ColumnDefinition {
-            kind: lancedb::table::ColumnKind::Physical,
-        }],
-    };
-
-    let mut maybe_embedded = MaybeEmbedded::try_new(reader, table_def, None).unwrap();
-
-    let result = maybe_embedded.next().unwrap().unwrap();
-    assert_eq!(result.num_columns(), 1);
-    assert_eq!(result.column(0).as_ref(), batch.column(0).as_ref());
-}